This application pertains-to the art of machine learning, and more particularly to the art of extracting conceptually meaningful clusters from a database of case records by implementation of symbolic empirical learning.
The invention is particularly applicable to a system for iteratively partitioning a database of case records into a tree of conceptually meaningful clusters and will be described with particular reference thereto although it will be appreciated that the invention has broader applications such as learning to recognize meaningful patterns of similarities and dissimilarities within a grouping of examples.
Earlier systems for partitioning case records into clusters suffer from two severe limitations: first, earlier systems fail to provide sufficient means for fully utilizing all available quantitative and qualitative case record data; and second, earlier systems fail to provide sufficient means for extracting conceptually meaningful clusters in the absence of prior domain-dependent knowledge.
The present invention contemplates a new system that overcomes both of the above-referred limitations and provides a domain-independent system for extracting conceptually. meaningful clusters from any database of case records. The preferred implementation iteratively builds a tree of conceptually meaningful clusters, hereafter referred to as a knowledge tree, and automatically assigns a unique conceptual meaning to each cluster in accordance with its unique pattern of typicality and exceptionality within the knowledge tree. Knowledge trees built by the system are particularly well suited for artificial intelligence applications such as pattern classification and nonmonotonic reasoning.